There is a lot of buzz around: going away from traditional data processing i.e., a relational database and persistent data in relational form being processed by a set of processes that capture, process (validate, summarize, re-format etc.,) and present (display on multiple format displays over multiple channels in verbal and multimedia formats) that data.
But where are we going? Object orientation of encapsulating data with its own operations to make loosely coupled application services those can be orchestrated to form business services with in an enterprise.... Those enterprise business services further choreographed to form a business to business flows across common interfacing models...
The traditional computer architecture that has a Processor that can process the data which is stored in a distinct Memory of the computer. The processor and memory are two distinct components of the basic architecture of the modern computer. When an "object" needs to be stored or shared between two different applications one should "serialize" we have Hibernate and JSON etc., formats developed for this data serialization...
At the same time, there is a trend that takes over to process the data more and more in parallel streams in the shared nothing style clusters to break the typical task into smaller pieces and summarize the results in a hierarchical fashion to arrive at final result. This can happen when the data becomes more and more unstructured with the help of objects!
Overall the trend means we are slowly going away from structured data stores in traditional relational databases and going nearer to natural language, fault tolerant and predictive data capture and processing (e.g., you can type any spelling on Google and it will return results for the right word!) and more visual and multimedia presentation of the information (in mashups, maps) with bi-directional interaction (like social, I can "like", "comment" etc on the presented data as feedback!)
That is just my view.... Businesses have to gear-up quickly to adapt to these trends!
Tuesday, April 26, 2011
Thursday, March 31, 2011
Leaving IBM...
Having worked with IBM for few months I have decided to move on. It has been a great experience to work with the world's no 1 software company. It is very special as IBM is celebrating its centenary as an organization.
I have been working in smart energy initiatives of Application Innovation Services division. Theme of my work at IBM is "Smarter Grids" (Follow the link to see what is a smart grid!)
I would take this opportunity to wish all my colleagues "All the best" until we cross our paths in this small IT world sometime, somewhere!
I have been working in smart energy initiatives of Application Innovation Services division. Theme of my work at IBM is "Smarter Grids" (Follow the link to see what is a smart grid!)
I would take this opportunity to wish all my colleagues "All the best" until we cross our paths in this small IT world sometime, somewhere!
Tuesday, March 22, 2011
Integration - Data or Information, Application
Having written a post on topological differences (EAI vs ESB) of integration long time back, and management approaches (centralized vs distributed) I am making an attempt to look at approach in terms of data integration vs application integration.
We have slightly touched upon this subject of Application Integration on one of the earlier post - Binding energy in software systems.
So, what is data integration? A typical Extract-Transform-Load - ETL, data migration, Change Data Capture - CDC, Master Data Management scenarios are classified as data integration. There are several platforms from big and small vendors to achieve this. (IBM InfoSphere/DataStage, Informatica, Microsoft, Oracle, Pervasive have suites/products under this head)
How is this different from traditional Application Integration? Application Integration focuses on integrating business process supporting the information workflows. Data Integration focus is primarily on the propagation and synchronization of data across the enterprise system landscape sometimes spanning into Cloud..
When the volumes are high, a data integration based approach has an advantage; If the process/workflow complexity is high and orchestration is needed to achieve the integration then one should look for an ESB/SOA style integration.
Enterprise Data Integration platforms are comparably similar to EAI platforms in their nature of technical architecture (hub/spoke based EAI topology) with a ETL hub that loads the data into a warehouse.
Of late, the buzz is toward real-time data integration based on CDC etc., let us see how it goes and changes the game!
We have slightly touched upon this subject of Application Integration on one of the earlier post - Binding energy in software systems.
So, what is data integration? A typical Extract-Transform-Load - ETL, data migration, Change Data Capture - CDC, Master Data Management scenarios are classified as data integration. There are several platforms from big and small vendors to achieve this. (IBM InfoSphere/DataStage, Informatica, Microsoft, Oracle, Pervasive have suites/products under this head)
How is this different from traditional Application Integration? Application Integration focuses on integrating business process supporting the information workflows. Data Integration focus is primarily on the propagation and synchronization of data across the enterprise system landscape sometimes spanning into Cloud..
When the volumes are high, a data integration based approach has an advantage; If the process/workflow complexity is high and orchestration is needed to achieve the integration then one should look for an ESB/SOA style integration.
Enterprise Data Integration platforms are comparably similar to EAI platforms in their nature of technical architecture (hub/spoke based EAI topology) with a ETL hub that loads the data into a warehouse.
Of late, the buzz is toward real-time data integration based on CDC etc., let us see how it goes and changes the game!
Friday, February 25, 2011
Architectural Approaches
Having written a small post on "Enterprise Solution Architecture" some time back on this blog now I want to touch up on the Architectural Styles/Approaches.
Typically the architecture discipline is about making "models" and naturally the architecture is thereby model-driven. Model Driven Architecture or MDA is an approach for building various models using the UML (Unified Modeling Language) for defining "structure" and "behavior" of a system being modeled.
But, a "model" can only represent a specific view point of the "system" being modeled. So, there should be a standard set of view points fit into a framework to describe a typical Enterprise. The open group's TOGAF and ADM tries to do that by defining specific "architectural layers" i.e, business, information, application and technology with multiple view points as functional view, security view, user view, communications view, management view etc., The ADM gives a methodology to select the key stakeholders and required viewpoints the architecture needs to be developed.
IEEE 1471 gives a recommended practice of "Architecture Descriptions" that generalizes specific frameworks in generating these models within the system's context, stakeholders and their specific needs.
But there is never been an architecture that related to Information Technology that is purely greenfield. There is always an "As-Is" architecture and a "To-Be" architecture that will be built based on current problem domain. SEI's ATAM is a tradeoff methodology to evaluate the architecture and evolve the architectures.
So, architecture discipline develops models that go out of sync by the time the solution goes live into production and starts solving the problem. It is really difficult to keep the models in sync with what is the reality on the field.
The Service Oriented Architecture i.e, SOA looks at the Enterprise as a set of loosely coupled services that interact to run the enterprise in its environment. This gave a rise to deploy and host the services in a marketplace like environment called "Cloud" that changes the paradigm of architecture into "Cloud Computing".
While a majority of architectures even in SOA are flow based, there is a different approach that is available is Event Driven Architecture or EDA. This approach looks at the events processing and events triggering various workflows in a business environment. With the complex-event-processing that correlates events over cause-effect, spacial or temporal dimensions it has specific uses in service management and Business Performance Management areas.
While solving complex problems all the above different architectural approaches can be used based on their fitment and availability of time and resources.
But the key is to have the right set of people, processes and tools in developing these architectural views!
Typically the architecture discipline is about making "models" and naturally the architecture is thereby model-driven. Model Driven Architecture or MDA is an approach for building various models using the UML (Unified Modeling Language) for defining "structure" and "behavior" of a system being modeled.
But, a "model" can only represent a specific view point of the "system" being modeled. So, there should be a standard set of view points fit into a framework to describe a typical Enterprise. The open group's TOGAF and ADM tries to do that by defining specific "architectural layers" i.e, business, information, application and technology with multiple view points as functional view, security view, user view, communications view, management view etc., The ADM gives a methodology to select the key stakeholders and required viewpoints the architecture needs to be developed.
IEEE 1471 gives a recommended practice of "Architecture Descriptions" that generalizes specific frameworks in generating these models within the system's context, stakeholders and their specific needs.
But there is never been an architecture that related to Information Technology that is purely greenfield. There is always an "As-Is" architecture and a "To-Be" architecture that will be built based on current problem domain. SEI's ATAM is a tradeoff methodology to evaluate the architecture and evolve the architectures.
So, architecture discipline develops models that go out of sync by the time the solution goes live into production and starts solving the problem. It is really difficult to keep the models in sync with what is the reality on the field.
The Service Oriented Architecture i.e, SOA looks at the Enterprise as a set of loosely coupled services that interact to run the enterprise in its environment. This gave a rise to deploy and host the services in a marketplace like environment called "Cloud" that changes the paradigm of architecture into "Cloud Computing".
While a majority of architectures even in SOA are flow based, there is a different approach that is available is Event Driven Architecture or EDA. This approach looks at the events processing and events triggering various workflows in a business environment. With the complex-event-processing that correlates events over cause-effect, spacial or temporal dimensions it has specific uses in service management and Business Performance Management areas.
While solving complex problems all the above different architectural approaches can be used based on their fitment and availability of time and resources.
But the key is to have the right set of people, processes and tools in developing these architectural views!
Wednesday, February 16, 2011
Analytics
Analytics is the "science of analysis" i.e., dividing a large set of data into certain "themes" and understanding various relationships between these to make out some business sense and take it for strategic advantage or for efficient operations is the field of Analytics.
In olden days, data that used to be stored in the computers is predominantly "Human generated transactional data" i.e., when an transaction happens between a producer and consumer, the data related to such transactions was stored in the software systems. This is relatively small amount of data.
As the days progressed, various "machine generated events" like a customer's website views, different clicks, data from automated sensors (like RFID etc., ) and various system log events are being stored for analyzing the behaviors.
This post is to enumerate different mathematical models and their uses in the field of business and web analytics.
1. Descriptive models: Used to classify the data into different groups. For example deriving the age of a person based on the first driving license date. Determining the sex based on height and weight etc., Focus is on as many variables as possible.
2. Predictive models: Used to find the causal relationships between the themes of data. Focus is on specific variables. These models give a probability of a set of outcomes.
3. Optimization/decision models: Used to derive the definite impact of certain decision and optimize the result within a set of constraints based on the data.
PMML = predictive model markup language from dmg is the xml based standard that can be used to exchange the models across multiple supporting applications.
The trend is in-database analytics that brings the data analytics into the database core engine and databases that are specifically built for the purpose of analytics based on columnar storage that makes the database an "analytical database" instead of a mere data storage and retrieval engine.
Oracle has published a good reference paper on this subject that can be found here -Predictive Analytics: Bringing tools to data.
over and above the thematic analysis there is an increasing demand for spacial and temporal analysis of the data. The field of analytics will converge into a single set of tools where one can analyse the data using the slicing and dicing functionality on all the dimensions of themes, spacial characteristics and temporal analysis at the same time with loads and loads of machine generated data is not far in the future....
Recently, I came across this paper that presents a framework for thematic, spacial and temporal analytics that can be possibly combined with data mining option....
In olden days, data that used to be stored in the computers is predominantly "Human generated transactional data" i.e., when an transaction happens between a producer and consumer, the data related to such transactions was stored in the software systems. This is relatively small amount of data.
As the days progressed, various "machine generated events" like a customer's website views, different clicks, data from automated sensors (like RFID etc., ) and various system log events are being stored for analyzing the behaviors.
This post is to enumerate different mathematical models and their uses in the field of business and web analytics.
1. Descriptive models: Used to classify the data into different groups. For example deriving the age of a person based on the first driving license date. Determining the sex based on height and weight etc., Focus is on as many variables as possible.
2. Predictive models: Used to find the causal relationships between the themes of data. Focus is on specific variables. These models give a probability of a set of outcomes.
3. Optimization/decision models: Used to derive the definite impact of certain decision and optimize the result within a set of constraints based on the data.
PMML = predictive model markup language from dmg is the xml based standard that can be used to exchange the models across multiple supporting applications.
The trend is in-database analytics that brings the data analytics into the database core engine and databases that are specifically built for the purpose of analytics based on columnar storage that makes the database an "analytical database" instead of a mere data storage and retrieval engine.
Oracle has published a good reference paper on this subject that can be found here -Predictive Analytics: Bringing tools to data.
over and above the thematic analysis there is an increasing demand for spacial and temporal analysis of the data. The field of analytics will converge into a single set of tools where one can analyse the data using the slicing and dicing functionality on all the dimensions of themes, spacial characteristics and temporal analysis at the same time with loads and loads of machine generated data is not far in the future....
Recently, I came across this paper that presents a framework for thematic, spacial and temporal analytics that can be possibly combined with data mining option....
Tuesday, January 11, 2011
Accountability and Authority
Back to fundamentals on the project management on this 11/1/11
The RACI matrix is a well known tool to identify the
Responsible
Accountable/Authority
Consulted and
Informed
parties involved in successful completion of a "Task".
Out of these four types of parties, there can be many responsible, many consulted and many informed about it but there should be one and ONLY ONE finally accountable for every given task within a project plan.
Why?
There should be one person in complete authority of a task who is empowered to take decisions during the task execution and makes sure the task in completed with the required quality within the time and within the effort allocated to the task. The person should have the final 'authority' to sign-off the deliverable.
In these days, technical architects are made accountable for tasks without necessary authority assigned.
Influencing people without authority and ability to be accountable for tasks is one of the key (soft) skills of a technical architect.
The RACI matrix is a well known tool to identify the
Responsible
Accountable/Authority
Consulted and
Informed
parties involved in successful completion of a "Task".
Out of these four types of parties, there can be many responsible, many consulted and many informed about it but there should be one and ONLY ONE finally accountable for every given task within a project plan.
Why?
There should be one person in complete authority of a task who is empowered to take decisions during the task execution and makes sure the task in completed with the required quality within the time and within the effort allocated to the task. The person should have the final 'authority' to sign-off the deliverable.
In these days, technical architects are made accountable for tasks without necessary authority assigned.
Influencing people without authority and ability to be accountable for tasks is one of the key (soft) skills of a technical architect.
Friday, December 17, 2010
Integration - Centralized or Distributed?
Let us imagine a big greenfield IT program that implements multiple packaged products, custom developed applications for an enterprise business.
There are multiple software vendors, package implementation partners and custom development teams, that are involved in the complete life cycle consisting of requirements analysis, architecture/design, code & test, integrate and deploy to production.
In such a scenario, how to deal with the interface development and integration?
Option 1: Centralized Integration Development
1. Agree the interfaces as step 1 of the program.
2. Let an independent group of people (let us call center of excellence) do the integration/interface work.
3. As the different products, applications are deployed the integration team will integrate them.
Option 2: Distributed Integration Development
1. Only have a integration back end (say ESB) be controlled by central team.
2. Each team working on different projects of the program will have their own interface development team.
3. As the different products, application are deployed the respective team will integrate them with the central ESB.
Which of the above options is better?
In my personal opinion, all the integration related activities are better done from a central COE.
Central COE can share the best practices, identify core patterns and leverage the skills.
Challenges in the COE model are acquiring and building the right skills and right sizing the COE for the program.
Any thoughts or ideas?
There are multiple software vendors, package implementation partners and custom development teams, that are involved in the complete life cycle consisting of requirements analysis, architecture/design, code & test, integrate and deploy to production.
In such a scenario, how to deal with the interface development and integration?
Option 1: Centralized Integration Development
1. Agree the interfaces as step 1 of the program.
2. Let an independent group of people (let us call center of excellence) do the integration/interface work.
3. As the different products, applications are deployed the integration team will integrate them.
Option 2: Distributed Integration Development
1. Only have a integration back end (say ESB) be controlled by central team.
2. Each team working on different projects of the program will have their own interface development team.
3. As the different products, application are deployed the respective team will integrate them with the central ESB.
Which of the above options is better?
In my personal opinion, all the integration related activities are better done from a central COE.
Central COE can share the best practices, identify core patterns and leverage the skills.
Challenges in the COE model are acquiring and building the right skills and right sizing the COE for the program.
Any thoughts or ideas?
Monday, December 6, 2010
Historize or Roll-Up
Information Technology is all about acquiring, processing, storing and presenting the "data" to the right people at right time to enable them to derive some meaningful information and in some cases useful intelligence or insight out of that data.
In usual business, a data point is captured only when there is a transaction that changes the data. So, each change to the data is captured, validated and stored. Such systems are called OLTP or On-Line Transaction Processing systems.
But, when the data is acquired at regular intervals in typical process control systems, not all the data points required to be stored; For example
a. the utilization of a system processor at every 5 seconds interval
b. Temperature of a steam turbine scanned at every 400 ms interval
In a typical process control system there will be several thousands of such data points scanned at very high frequency, typically every second.
What to do with all this data?
In a standard relational database storing all this data in raw format will be simply impractical. So, there are two methods to make some sense out of such "time series" data.
1. Historize the data using a process historian. A process historian uses a compression algorithm that only stores a data point only when there is a deviation beyond a set limit using variety of algorithms like straight line interpolation (SLIM1, 2, 3) or swinging door compression etc., to achieve a high degree of compression in storing the time series data.
2. Roll-up data on a periodical (i.e., hourly data for few weeks) basis to store max, min, average, standard deviation etc., values in one single record per data point. A next level roll-up of data can happen for a longer time interval (i.e., daily data for several years) This multi-level roll-up data can be used for historical trending purposes.
There are advantages of both methods. Recently I have seen a patent on dynamic compression of system management data which is interestingly putting the Historization with multiple compression algorithms for storing the systems management data.
In usual business, a data point is captured only when there is a transaction that changes the data. So, each change to the data is captured, validated and stored. Such systems are called OLTP or On-Line Transaction Processing systems.
But, when the data is acquired at regular intervals in typical process control systems, not all the data points required to be stored; For example
a. the utilization of a system processor at every 5 seconds interval
b. Temperature of a steam turbine scanned at every 400 ms interval
In a typical process control system there will be several thousands of such data points scanned at very high frequency, typically every second.
What to do with all this data?
In a standard relational database storing all this data in raw format will be simply impractical. So, there are two methods to make some sense out of such "time series" data.
1. Historize the data using a process historian. A process historian uses a compression algorithm that only stores a data point only when there is a deviation beyond a set limit using variety of algorithms like straight line interpolation (SLIM1, 2, 3) or swinging door compression etc., to achieve a high degree of compression in storing the time series data.
2. Roll-up data on a periodical (i.e., hourly data for few weeks) basis to store max, min, average, standard deviation etc., values in one single record per data point. A next level roll-up of data can happen for a longer time interval (i.e., daily data for several years) This multi-level roll-up data can be used for historical trending purposes.
There are advantages of both methods. Recently I have seen a patent on dynamic compression of system management data which is interestingly putting the Historization with multiple compression algorithms for storing the systems management data.
Sunday, November 28, 2010
Energy Storage - Pumped Storage Hydro
Q: How to store the energy?
A: In its potential form...
When electricity is produced from an hydro electric plant, during off-peak hours of demand, the produced electricity can be used to pump the water to a higher reservoir using a reversible turbine/generator assembly.
The non-conventional "wind turbines" or "solar cells" can also be used to pump the water to a higher reservoir, thereby making the electricity production from them more reliable to connect the generated energy to the electricity Grid!
Once the water is available in higher reservoir, electricity can be generated as and when needed depending on the demand.
This is the largest capacity form of stored (potential) energy available for a Grid Connected storage.
In India we have one 1000MW plant at Tehri Dam.
In my personal opinion, this is one of the best and environmental friendly solution to store the electricity!
A: In its potential form...
When electricity is produced from an hydro electric plant, during off-peak hours of demand, the produced electricity can be used to pump the water to a higher reservoir using a reversible turbine/generator assembly.
The non-conventional "wind turbines" or "solar cells" can also be used to pump the water to a higher reservoir, thereby making the electricity production from them more reliable to connect the generated energy to the electricity Grid!
Once the water is available in higher reservoir, electricity can be generated as and when needed depending on the demand.
This is the largest capacity form of stored (potential) energy available for a Grid Connected storage.
In India we have one 1000MW plant at Tehri Dam.
In my personal opinion, this is one of the best and environmental friendly solution to store the electricity!
Thursday, November 18, 2010
smarter grids
I have been at a conference related to Smart Grids at Delhi last week. There was a question asked by a BA student to me "What is this smart grid all about?"
I will try to answer this question as simply as I can in this post.
Electricity traditionally supplied to consumers from its generating plants using the "power GRID". The interconnected network of generating plant -> transmission grid (high voltage) -> distribution grid (medium voltage) -> Consumer (at 220Volts) is the power grid.
Due to the amount of investment into this core infrastructure of GRID, it has been a public sector (government) agency that built the grid. The way consumers buy or pay for the energy is dictated by the utility company.
As the infrastructure development slowly got de-regulated and more and more private participation is into power generation and "open access" to the power across the grid is being made available for consumers.
"Energy" is freely available in the nature in the form of Sun light, wind, tidal, flow of a river, coal, nuclear etc., forms but making it available in a house to the lights, fans, ACs and several electrical appliances like pump sets etc., costs money.
Energy has an initial "conversion" cost i.e., converting fuel to heat in a boiler and heat to mechanical movement of a turbine and the mechanical energy to electricity by a generator. Then it need to pass through the transmission and distribution channels to the consumer which adds a "transportation and distribution" cost. Finally the consumption at each end point is measured by a Meter that involves some costs of metering, billing and collection of bills through operating some channels like collection kiosks, bank direct debit etc., from the final consumer.
How is all this related to smartness of the GRID?
A customer has not much a role in the whole process... He uses the Electricity and pays the bill monthly... It is a one-way flow of information and energy!
As the demand for energy increases and the fossil fuel like coal decreases in availability one should think of methods to wisely use the energy. A smarter grid is an enabler for wiser generation, distribution and consumption of the energy.
A "smart meter" that can measure more accurate consumption and power quality at more frequent interval (e.g every one hour) gives a better understanding of consumption patterns for the consumer and utility, there by the energy pricing can be more dynamic as different price can be applied for peak hours and another price for non-peak hours.
A "smart meter" enables micro grids. Where ever possible small commercial customer can have their own "solar" or "wind" generation capability and when they have excess of energy they can reduce the consumption from the grid or even feed the energy back to the grid. So, the meter can measure two way i.e., import and export of the energy from and to the grid.
This makes the distribution grid to be more responsive to the demand and supply scenario. A distribution grid should instrument each transformer, feeder and substation to measure power quality and other key parameters for efficient use of the equipment by minimizing the asset maintenance costs and failures of the equipment. It will also ensure the energy lost over the transportation to a minimum. (Currently globally a 50% of generated energy never reaches any customer!!)
An interconnected set of devices they can exchange information along with the energy in both directions for the purpose of making intelligent decisions for wise consumption of energy/utility makes up a smart grid.
A customer is more informed about the prices of energy he is using at different times of the day, Utility can control some high power consuming equipment at customer premise remotely, information exchanged between the customer, distribution, transmission and generation entities in bi-directional, automated mode.
It will finally lead to conserving the energy, managing the demand and supply balance in a wise manner and finally make the planet EARTH more greener! That is the whole idea of smarter grids. Advancements in telecommunication and information technology enable the transformation of power grids into smarter grids.
I hope this post is simple and smart enough to explain what is a "Smarter Grid" to a novice.
I will try to answer this question as simply as I can in this post.
Electricity traditionally supplied to consumers from its generating plants using the "power GRID". The interconnected network of generating plant -> transmission grid (high voltage) -> distribution grid (medium voltage) -> Consumer (at 220Volts) is the power grid.
Due to the amount of investment into this core infrastructure of GRID, it has been a public sector (government) agency that built the grid. The way consumers buy or pay for the energy is dictated by the utility company.
As the infrastructure development slowly got de-regulated and more and more private participation is into power generation and "open access" to the power across the grid is being made available for consumers.
"Energy" is freely available in the nature in the form of Sun light, wind, tidal, flow of a river, coal, nuclear etc., forms but making it available in a house to the lights, fans, ACs and several electrical appliances like pump sets etc., costs money.
Energy has an initial "conversion" cost i.e., converting fuel to heat in a boiler and heat to mechanical movement of a turbine and the mechanical energy to electricity by a generator. Then it need to pass through the transmission and distribution channels to the consumer which adds a "transportation and distribution" cost. Finally the consumption at each end point is measured by a Meter that involves some costs of metering, billing and collection of bills through operating some channels like collection kiosks, bank direct debit etc., from the final consumer.
How is all this related to smartness of the GRID?
A customer has not much a role in the whole process... He uses the Electricity and pays the bill monthly... It is a one-way flow of information and energy!
As the demand for energy increases and the fossil fuel like coal decreases in availability one should think of methods to wisely use the energy. A smarter grid is an enabler for wiser generation, distribution and consumption of the energy.
A "smart meter" that can measure more accurate consumption and power quality at more frequent interval (e.g every one hour) gives a better understanding of consumption patterns for the consumer and utility, there by the energy pricing can be more dynamic as different price can be applied for peak hours and another price for non-peak hours.
A "smart meter" enables micro grids. Where ever possible small commercial customer can have their own "solar" or "wind" generation capability and when they have excess of energy they can reduce the consumption from the grid or even feed the energy back to the grid. So, the meter can measure two way i.e., import and export of the energy from and to the grid.
This makes the distribution grid to be more responsive to the demand and supply scenario. A distribution grid should instrument each transformer, feeder and substation to measure power quality and other key parameters for efficient use of the equipment by minimizing the asset maintenance costs and failures of the equipment. It will also ensure the energy lost over the transportation to a minimum. (Currently globally a 50% of generated energy never reaches any customer!!)
An interconnected set of devices they can exchange information along with the energy in both directions for the purpose of making intelligent decisions for wise consumption of energy/utility makes up a smart grid.
A customer is more informed about the prices of energy he is using at different times of the day, Utility can control some high power consuming equipment at customer premise remotely, information exchanged between the customer, distribution, transmission and generation entities in bi-directional, automated mode.
It will finally lead to conserving the energy, managing the demand and supply balance in a wise manner and finally make the planet EARTH more greener! That is the whole idea of smarter grids. Advancements in telecommunication and information technology enable the transformation of power grids into smarter grids.
I hope this post is simple and smart enough to explain what is a "Smarter Grid" to a novice.
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